We study online scheduling of jobs to minimize the flow time and stretch on parallel machines. We consider algorithms that are given extra resources so as to compensate for the lack of future information. Recent results show that a modest increase in machine speed can provide very competitive performance; in particular, using $O(1)$ times faster machines, the algorithm SRPT (shortest remaining processing time) is 1-competitive for both flow time [C. A. Phillips et al., in Proceedings of STOC, ACM, New York, 1997, pp. 140–149] and stretch [W. T. Chan et al., in Proceedings of MFCS, Springer-Verlag, Berlin, 2005, pp. 236–247] and HDF (highest density first) is $O(1)$-competitive for weighted flow time [L. Becchetti et al., in Proceedings of RANDOM-APPROX, Springer-Verlag, Berlin, 2001, pp. 36–47]. Using extra unit-speed machines instead of faster machines to achieve competitive performance is more challenging, as a faster machine can speed up a job but extra unit-speed machines cannot. This paper gives a nontrivial relationship between the extra-speed and extra-machine analyses. It shows that competitive results via faster machines can be transformed to similar results via extra machines, hence giving the first algorithms that, using $O(1)$ times unit-speed machines, are 1-competitive for flow time and stretch and $O(1)$-competitive for weighted flow time.